Method to determine predictive tests and device applying same to lubricant formulations

ABSTRACT

The present invention is directed to a method that determines the necessary and sufficient tests to relate a variety of apparently non-related tests to desired final test results. The present invention also provides a method to determine those tests which, having been shown capable to be used in a high-throughput environment, are able to predict end-use qualification test results for lubricants, greases or industrial fluids. As a corollary, the present invention provides a method to select lubricant formulations and components based on apparently non-related, but predictive tests. In an applied example, the present invention is directed to a device and method that produces and evaluates formulated lubricants, functional fluids, and greases by determining previously unknown relationships between Intermediate Tests and End-use Tests.

CROSS REFERENCE TO RELATED APPLICATION

This application claims the benefit of U.S. Provisional Application No.60/703,732 filed Jul. 29, 2005.

FIELD OF THE INVENTION

The present invention is directed to a method that determines thenecessary and sufficient tests to relate a variety of apparentlynon-related tests to desired final test results. The present inventionalso provides a method to determine those tests which, having been showncapable to be used in a high-throughput environment, are able to predictend-use qualification test results for lubricants, greases or industrialfluids. As a corollary, the present invention provides a method toselect lubricant formulations and components based on apparentlynon-related but predictive tests. In an applied example, the presentinvention is directed to a method and a device that produces andevaluates formulated lubricants, functional fluids, and greases bydetermining previously unknown relationships between Intermediate Testsand End-use Tests.

BACKGROUND OF THE INVENTION

Lubricant, grease and industrial fluid formulation research has longbeen acknowledged as a combination of art and science. Formulationresearch presents a nearly overwhelming number of variables for eachpossible application. Even within a given application area, a widevariety of base fluids may be used. For instance, base fluids areproduced to meet required specifications. These base oils are classifiedby the American Petroleum Institute (API) as Group I, Group II, GroupIII, Group IV and Group V, which designate parametric boundaries forviscosity index, sulfur content, amount of non-paraffins and the like.However, the actual chemical composition of a base oil that meets aspecific API group criteria may vary significantly from base oil to baseoil.

Compounding these issues are the vast variety of chemical additiveswhich have become necessary components in today's modern lubricants. Forexample, lubricants commonly include additives for corrosion control,metal passivation, extreme pressure resistance, viscosity modification,detergency, acid control, etc. While one might correctly assume that thechemistries among these functional groups may vary widely, it is alsorecognized that the chemistries within each of these functional additivegroups may vary significantly. While the properties of any one additivein any one base oil may be relatively well known, combining additivesmay have unexpected (beneficial or undesired) chemical interactions.

Lubricant research might be somewhat simplified if it was limited tosolving this myriad of chemical interactions between additives and baseoils. But in the real world, varying engine configurations presentsunique flow and heat transfer properties that cause even a standardizedlubricant to react differently. Currently, equipment manufacturersrequire that actual engine or machinery tests verify the applicabilityof a candidate lubricant formulation. Indeed, many Original EquipmentManufacturers (OEM) of engines or other equipment that employlubricants, greases or functional fluids, have their own unique test to“qualify” the candidate product. Tests such as the European UnionAssociation des Constructeurs Europeens d'Automobiles (ACEA) standards,or the United States American Petroleum Instititute (API) andInternational Lubricant Standards Approval Committee (ILSAC) standardsrequire large quantities of the candidate fluids tested over weeks oftime under actual full-scale engine conditions. These tests are timeconsuming and costly.

Lubricant researchers often employ a number of lowest-common-denominatorbench tests to attempt to predict how a lubricant would fare inreal-world conditions. Such bench tests are designed to provide in alaboratory environment a measure of a property or performance feature ofa lubricant sample. The researcher attempts to use the bench test tomake a laboratory model of the conditions of actual engines orequipment. Usually, the scope of the bench tests is limited toattempting to re-create one specific aspect of the equipment's operatingenvironment. Not being able to exactly match the intense pressure, heat,friction, load and other conditions of operating equipment, researchersmake assumptions to design bench tests to isolate the variable ofinterest. Unfortunately, it is generally acknowledged that bench testsare, at best, weakly predictive of the single dimension of equipmentconditions they attempt to mimic.

Examples of these tests are as follows: ASTM D2266 (Four Ball method forwear preventive characteristics of lubricating grease), ASTM D2272(Oxidation stability of by rotating bomb), ASTM D2596 (Four Ball methodfor measurement of load carrying capacity of lubricating grease), ASTMD2783 (Four Ball method for measurement of extreme-pressure propertiesof lubricating fluids), ASTM D4172 (Four Ball method for wear preventivecharacteristics of lubricating fluids), ASTM D4742 (Thin-film oxygenuptake test), ASTM D6138 (Emcor test for determination of corrosionpreventive properties of lubricating grease under dynamic wetconditions), ASTM D6186 (Pressure differential scanning calorimetrymethod for oxidation induction time of lubricating oils) along with thenumerous other tests specified in various lubricant oil or greasespecifications.

These tests too often show poor correlation to real-world results. Sincethese tests tend to investigate along a single dimension, they limitopportunities to discover positive or negative chemical interactions.Moreover, it is difficult, if not impossible, to determine whichcombination of tests, if any, would predict a binary pass/fail resultfor any specific OEM's end-use test. These tests would most likely notgive a graduated view of which base oils, additives or formulationswould better pass a given OEM end-use test.

The present invention addresses these, and many other issues.Specifically, the present invention provides a method to determine whichlaboratory scale tests are predictive of real-world results or OEMend-use tests. While the inventors believe that best candidatelaboratory-scale tests would be those that produce significant amountsof data, the present invention also details a method of using data incurrently existing databases to predict which test, or functionalcombination of tests, would best predict OEM end-use tests or real worldperformance results.

One feature of the present invention is that it provides a means todetermine which tests, useful in a laboratory setting, would besufficient to predict the desired end-use qualifying test results.Another feature of the present invention is that it demonstrates amethod and a device to predict and select voluminous data-producingtests that will mimic the lubricant bench test results or the end-usequalifying test results. As a corollary, the present invention providesa method to determine which tests, capable of being used in ahigh-throughput environment, are able to predict end-use qualificationtest results for lubricants oils and greases. Still yet another featureof the present invention is its ability to employ historical databasesof bench test results to select combinations of those bench tests, withand without high-throughput tests, that more accurately predict theend-use qualifying test results.

One embodiment of the present invention employs patternrecognition-based modeling to guide adaptive learning systems to derivecorrelative models by learning from data. The present invention's use ofiterative learning leads to converged functional classifications and/orcorrelations between independent and dependent variables.

Throughout this application, the inventors use of the word lubricant (orits derivatives) also refers to lubricants, greases, and various typesof functional fluids (and their respective derivatives).

The current state of the art for the formulation of lubricants requiresextensive formulator experience to select the optimum combination ofadditives and base stocks. The possible combinatorial space is quitelarge consisting of many different base oils and “functional families”of additives (e.g., antiwear, antioxidants, antifoaming, viscositymodifiers, dispersants, thickeners, detergents, etc.). Each functionalfamily contains numerous different chemistries to achieve the desiredfunction. Further complicating the formulation discovery process is thatthe base oils for the lubricant vary widely from highly naphthenic APIGroup I base oils to high purity PAO to even non-hydrocarbon basedfluids such as silicones. Another complication is that the additivefunctional families may react differently to different base oilcombinations. Indeed, one other well-known problem is that lubricantformulation chemistries are not always linear—that is, an interpolatedblend of two successful lubricant chemistries does not always produce aproduct able to pass the same tests.

Creating a new or “step-out” lubricant formulation is severely limitedby the extensive in-place engine or machinery testing that eachsuccessful candidate lubricant must pass. On average, each individualtest costs between $10,000-$150,000. Sole reliance on expensive,large-scale testing to develop new lubricants (and greases), resultsmore often in incremental formulation improvements and limits theinclusion of new, experimental components in new formulations since theyrequire more extensive testing. Overall, sole reliance on expensivelarge-scale testing confines experimentation more often to the limitedknown-formulation performance, and it is likely that opportunities forstep-out improvements in formulation technology for lubricants,functional fluids or greases are not captured.

The introduction of intermediate bench tests to lubricant formulationresearch can further complicate the process. Lubricant bench-testsattempt to mimic essential portions of the engine's or industrialequipment's operation, usually limiting themselves to a single dimension(e.g. acid value increase in a stability test at certain temperature.For example, engines may vary significantly within a product category(commercial, personal vehicle, aviation, marine or stationary industrialengines), let alone compared to other types of equipment such as,gearboxes, pumps, compressors, circulating systems and others. Anyindividual bench test predictive for any one engine is almost certainlynot predictive of other engines or machinery.

A further complication is that equipment manufacturer's lubricantqualification tests differ even for similar equipment and are oftenchanged on a frequent basis to reflect updated equipment technology.Typically, lubricant bench-testing is called upon to predict a largerange of possible outcomes. While lubricant bench-tests are intended toallow an inexpensive measure of predictability for the more expensivelarge-scale tests, understanding and interpreting the correlationsbetween bench tests and the final engine or machinery tests has oftenproven to be difficult. Years of experience, combined with aformulator's intuition, can help to link a successful set of bench teststo a successful large-scale, end-use test or tests. However, even uponentering the 21st century, the formulation of lubricants, functionalfluids or greases remains both an art and a science.

Among other features, the present invention provides a uniqueopportunity to apply high throughput techniques to lubricant research.Researchers have traditionally attempted to use a series of bench teststo determine the potential performance of a formulated lubricant,functional fluid or grease candidate in end-use tests. Commonly usedbench tests include wear, viscosity, thermal and oxidative stability,deposit control, elastomer compatibility, filterability, friction,volatility, foam and air release, corrosion and rust, miscibility,solubility and homogeneity and visual appearance. However, these benchtests were seldom adaptable to producing large amounts of data in shortperiods of time as they often required large lubricant sample volumes,long test times, severe test conditions or combinations of all three.Even if one could easily adapt these bench tests to produce largevolumes of data in a short period of time, there is no reason to believethat they would correctly predict actual success on end-use qualifyingtests.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 is a diagram illustrating the Elemental Sets for the currentmethod that relate lubricant “intermediate” testing to final end-usequalifying test results.

FIG. 2 is a diagram illustrating the operation of a simple neuralnetwork.

FIG. 3 is a graphical representation of the wear rate versus the amountof Extreme Pressure Additive found in one type of formulation.

FIG. 4 through FIG. 9 are graphical representations of each Mobil thinfilm oxidation (tfo) test (numbers 1 through 6) plotted against theVolkswagen TDi2 (VWTDi2) test results showing no predictive ability ofthe individual tfo tests.

FIG. 10 presents three graphical representations of the tfo1 vs. thetfo5 test vs. the VWTDi2 test (on the z-axis). Each individual graph hasoverlays showing the predictive models provided by either linear ornon-linear correlation functions.

FIG. 11 is a representation of a mechanistic model for the formation ofsludge, varnish and deposit formation in lubricants.

FIG. 12 graphically depicts the predictive capability of the fresh-oiland Catalytic Oxidation Test aged oil models to the VWTDi2 End-use test.

FIG. 13 is a component drawing demonstrating the interconnection of manyof the derived intermediate tests in a single apparatus.

SUMMARY OF THE INVENTION

A method to determine which members, individually or in combination, ofan Elemental Set of Intermediate Tests are predictive of an ElementalSet of End-Use Test Results, the method comprising:

-   -   providing an Elemental Set of Samples having at least a first        member and a second member;    -   providing an Elemental Set of End-Use Test Results for said        Elemental Set of Samples, the Elemental Set of End-Use Tests        Results, having at least a first and a second member, such that        the first member of said Elemental Set of End-Use Test Results        corresponds to said first member of said Elemental Set of        Samples and said second member of said Elemental Set of End-Use        Tests Results corresponds to said second member of said        Elemental Set of Samples, wherein said first member of each set        is not the same as the second member of each set;    -   selecting an Elemental Set of Intermediate Tests having a        plurality of members;    -   obtaining from said selected Elemental Set of Intermediate        Tests, a set of Intermediate Test Results; and    -   subjecting the Intermediate Test Results, individually or in        combination, to regression analysis to determine which of the        members of the Elemental Set of Intermediate Tests is predictive        of the Elemental Set of End-Use Test Results for the Elemental        Set of Samples.

The present invention is directed to a method that determines thenecessary and sufficient tests to relate intermediate tests to finalend-use qualifying tests for lubricants.

Another feature of the present invention is that it demonstrates amethod to predict and select voluminous data-producing tests that willemulate the lubricant bench test results or the end-use qualifying testresults.

As a corollary, the present invention provides a method to determinewhich tests, capable to be used in a high-throughput environment, areable to predict end-use qualification test results for lubricants.

In a more specific embodiment, the present invention is directed to amethod that produces and evaluates formulated products that would passend-use qualifying tests for lubricants.

In another embodiment, the present invention relates to a method thatrapidly, in parallel or serial, tests small samples of formulatedproducts for at least one of following properties or performancefeatures:

-   -   1. Wear    -   2. Friction    -   3. Viscosity    -   4. Oxidative Stability    -   5. Thermal Stability    -   6. Sludge, varnish and deposit formation    -   7. Elastomer compatibility    -   8. Volatility    -   9. Corrosion and Rust    -   10. Miscibility or solubility    -   11. Visual appearance    -   12. Chemical Composition (Polarity)    -   13. Scuffing    -   14. Acidity Increase    -   15. Soot Formation    -   16. Storage Stability    -   17. Hydrolytic Stability    -   18. Color-Light Stability    -   19. Low Temperature Fluidity    -   20. Demulsibility    -   21. Emulsibility    -   22. Foam Formation    -   23. Air Entrainment    -   24. Acute Toxicity        and then determines which of those tests (or functional        combinations thereof) are predictive of some other desired        result(s).

In another embodiment, the present invention relates a method todetermine “step-out” formulations or additives from known usefulintermediate tests and end-use results.

DETAILED DESCRIPTION OF THE INVENTION

The present invention provides a techniques developed to rapidly produceand evaluate lubricant candidates to determine their probable success inbench testing, and end-use engine or equipment testing. One feature ofthe present invention is a method that determines which types ofintermediate tests correlate to specific end-use qualifying tests. Morespecifically, the present invention provides a method to determine thosetests which, having been shown capable to be used in a high-throughputenvironment, are able to predict end-use qualification test results forlubricants. A second feature of the present invention is that itprovides a method to combine historical or new bench test data andselect combinations of bench tests that are predictive of specificend-use lubricant qualification tests.

A third feature of the present invention is that once appropriateintermediate tests are selected, mathematical models and variableselection algorithms may be employed to correlate those results toselected performance features. This methodology results in extractingfeatures from the experimental results, yielding information that enableformulations that satisfy the end-use qualification tests. Hence thepresent invention can predict the components for formulations that willlead to successful end-use qualifying test results.

Referring to the Figures, FIG. 1 is a diagram indicating the stages ofthe current method that relate lubricant “intermediate” testing to finalend-use qualifying test results. The diagram lists three Elemental Sets:a “Samples” or “Component” Elemental Set, an “Intermediate Tests”Elemental Set and an “End-Use Test Results” Elemental Set. When themembers of any two of these Elemental Sets are defined, the method ofthe current invention allows one to determine the more predictivemembers of the third Elemental Set.

For the purposes of the present invention, any item that would fit intoany of the boxes of FIG. 1 would be considered a member of thatElemental Set. Therefore, any one test result of an end-use qualifyingtest would be a member of the Elemental Set of End Use Test Results.Similarly, each sample, or component of a sample, would be a member ofthe Elemental Set of all Samples or Components. For example, in oneembodiment of the present invention, the candidate samples and theirformulations would constitute the “Sample” Elemental Set. How each ofthose samples performed on the end-use qualifying tests would populatethe “End-use Results” Elemental Set. The members of the “End-useResults” Elemental Set may include a simple pass/fail determination ofthe end-use qualifying test or may give more detailed indications of thequality of passing the test. The present invention applies equally wellin either situation.

Once these known inputs are selected for the Samples Elemental Set andEnd-Use Results Elemental Set, a set of intermediate tests is chosen toform the Intermediate Tests Elemental Set. While this choice could bemade randomly, one of ordinary skill in the art may also choose a set ofIntermediate Tests which they expect might be more predictive than arandom selection. These tests may be classical bench tests, but in thisembodiment, it is preferred that the tests be capable of being performedin a high throughput environment.

While several useful examples of specific tests created by this methodare provided later in this application, any set of tests may be applied.A preferred set of tests would be those tests that would produce highquantities of data over large ranges. For example, FTIR testing, UVtests and mass spectrometry tests may be considered particularly usefulin that they readily provide significant amounts of information for eachsample. One of ordinary skill in the art would easily be able to discernother tests usable in a high throughput environment that would alsoproduce numerous members of the Intermediate Test Results Elemental Set.

The present invention determines which of the selected set ofIntermediate Tests more successfully relates one of the definedElemental sets to the other defined Elemental Set. While the presentmethod works as long as each Elemental Set has at least two members, themore members of each Elemental Set, the better the predictive results ofthe method.

Once an Elemental Set of Intermediate Tests has been selected, thoseintermediate tests are run on each sample. These results are thenanalyzed using any variety of modeling techniques. Some simplenon-limiting examples of modeling techniques include generalized linearregressions such as multiple linear regression, principal components,ridge regression, each of which is encompassed by this invention. Formore complex data sets non-linear regression techniques such as neuralnetworks may be employed. One of ordinary skill in the art is well awareof other modeling techniques that could easily be employed with thepresent invention.

The inventors note that most regression models intended to assess apass/fail criteria are fully encompassed in this discovery. For example,neural networks, principal component regression, and other linear ornon-linear regression models could be written in the general form:

-   -   e_(A)=F(x¹ _(A), x² _(A), x³ _(A), . . . , x^(n) _(A))+ε or        e_(A)=F(X_(A))+ε; where X_(A)=(x¹ _(A), x² _(A), x³ _(A), . . .        , x^(n) _(A))    -   Predicted Performance=F(X_(A))=F(x¹ _(A), x² _(A), x³ _(A), . .        . , x^(n) _(A))    -   ε: Error term, comprising of errors in the e_(A), x^(i) _(A) and        F lack-of-fit    -   If Passing Criterion is, e.g.,: e_(A)<F₀    -   Predicting Passing Criteria based on x^(i) _(A): F(x¹ _(A), x²        _(A), x³ _(A), . . . , x^(n) _(A))<F₀,        where “F” is a continuous performance-predicting function of “n”        variables, “A” is one member of the Elemental Set of Samples, x¹        _(A) is the result of the first Intermediate Test performed on        Sample “A”, “e_(A)” is the member of the End User Test Elemental        set corresponding to Sample “A”, and F₀ is the pass/fail        threshold. For such a model, there exists a subspace, or region,        in the (x¹, x², X³, . . . , x^(n)) “space” (the space of        Intermediate Tests results) that maps, through the function F,        to the passing criteria. This can be extended to multiple        criteria by choosing different values of F₀, thereby creating        different boundaries in the Intermediate Test Space which        delimit different levels of lubricant performance.

As more criteria levels are added, in the limit, all known regressionmodels are a subset of this generalized boundary-based classificationapproach. As non-limiting examples, Principal Component Regression,Multiple Linear Regressions and Neural Net Regressions fit into thismodel. One embodiment of the present invention uses a back propagatingneural net. Back-propagation (or “backprop”) neural nets are comprisedof inter-connected simulated neurons. A neuron is an entity capable ofreceiving and sending signals and is simulated by software algorithms ona computer. Each simulated neuron (i) receives signals from otherneurons, (ii) combines these signals, (iii) transforms this signal and(iv) sends the result to yet other neurons. Typically, a weight,modifying the signal being communicated, is associated with eachconnection between neurons.

These concepts may be more fully explained by FIG. 2 which represents asimplified neural net showing one layer. This figure uses the sameearlier convention where x^(n) represents the nth Intermediate testresult for a specific sample. A neural net attempts to find the valuesof each coefficient (a, b, c, d, etc.) that will map to the End UseResult for that Sample. While we commonly think of linear regressions(as shown in the first level), each level applies a function (forexample, a sigmoid) upon each of the coefficients to produce a new setof coefficients (w₁, w₂, w₃, w₄, etc.). This may occur for as manylayers as desired.

Once the coefficients (a, b, c, d,) are determined such that they mapthe Intermediate Tests Results to the Final End Use Test Result for thatspecific Sample to a desired degree of Error, the neural net thenapplies those coefficients to the Intermediate Tests results for thenext Sample. If the coefficients do not map to the End Use Test for theSecond Sample to an acceptable error level, then weights are applied toeach coefficient and the process is repeated until coefficients aredetermined that produce an acceptable error level for both samples. Thisprocess is continued until a mapping function is determined thatproduces an acceptable level of error between all Intermediate Tests andtheir respective End Use Test Results.

The “information content” of the net is embodied in the set of all theseweights that, together with the net structure, constitute the modelgenerated by the net. The back-prop net has information flowing in theforward direction in the prediction mode and back-propagated errorcorrections in the learning mode. Such nets are organized into layers ofneurons. Connections are made between neurons of adjacent layers: aneuron is connected so as to receive signals from each neuron in theimmediately preceding layer, and to transmit signals to each neuron inthe immediately succeeding layer.

A minimum of three layers is utilized. An input layer, as its nameimplies, receives input. One or more intermediate layers (also calledhidden layers because they are hidden from external exposure) liebetween the input layer and the output layer which communicates resultsexternally. Additionally, a “bias” neuron supplying an invariant outputis connected to each neuron in the hidden and output layers. The numberof neurons used in the hidden layer depends on the number of the inputand output neurons, and on the number of available training datapatterns. Too few hidden neurons hinder the learning process, and toomany degrade the generalizing capability of the net.

An outcome from a given input condition is generated in the followingway. Signals flow only in the forward direction from input to hidden tooutput layers. A given set of input values is imposed on the neurons inthe input layer. These neurons transform the input signals and transmitthe resulting values to neurons in the hidden layer. Each neuron in thehidden layer receives a signal (modified by the weight of thecorresponding connection) from each neuron in the input layer. Theneurons in the hidden layer individually sum up the signals they receivetogether with a weighted signal from the bias neuron, transform thissum, and then transmit the result to each of the neurons in the nextlayer.

Ultimately, the neurons in the output layer receive weighted signalsfrom neurons in the penultimate layer and the bias, sum the signals, andemit the transformed sums as output from the net. The net is trained byadjusting the weights in order to minimize errors. In the learning (ortraining) mode, the net is supplied with sets of data comprised ofvalues of input variables and corresponding target outcomes. The weightsfor each connection are initially randomized. During the trainingprocess, the errors (which are the differences between the actual outputfrom the net and the desired target outcomes) are propagated backwards(hence the name “back-propagation”) through the net and are used toupdate the connecting weights. Repeated iterations of this operationresult in a converged set of weights and a net that has been trained toidentify and learn patterns (should they exist) between sets of inputdata and corresponding sets of target outcomes. More informationconcerning making and using neural nets may be found at J. Leonard & M.A. Kramer, Computers and Chemical Engineering, v. 14, #3, pp. 337-341,1990.

Neural nets, after being trained on data, result in a correlative modelthat predicts a quantitative outcome when presented with a set ofindependent parameters as input. This quantitative result enablesdetermination of a set of desirable input variables which maximize theperformance (i.e., model outcome). This is accomplished by deployingsuitable optimization techniques, viz., genetic algorithms.

Once an acceptable mapping is achieved by neural networks to estimatethe function F above, a classification scheme is selected to limit themapping function (and therefore the contours of the selected region in xspace) to a particular set of mappings. For example, the uses of thisinvention develops a neural network model that relates families of X_(c)vectors (wherein each X_(c) vector represents the results of selectedintermediate tests on a single Sample, sometimes known as a “calibrationset” or “training set”) to the corresponding engine test results. Then,future bench test screener result vectors X_(p) (where X_(p) is theresults of the intermediate tests on a previously unknown Sample, X_(p)is sometimes known as the “prediction set”) can be tested with theneural network function F, against the F₀ threshold. Some will “pass”,some will “fail” the neural network model.

It is possible to create a map of the pass/fail regions by usingregression techniques. For example, one way of implementing this, is to“run” large numbers of simulated X_(t) (i.e., a universe of Sample orComponent Sets) vectors, then build the corresponding “pass” and “fail”regions in X_(C) space. Those classification models would yield the sameprediction results as the neural network regression model. Once F isavailable, it can be applied across the screener space to make contoursto predict varying pass or fail results at various pass/fail criteria.

Because of the vast number of variables (Intermediate Tests) that couldbe employed in lubricant formulations, the inventors have found itperformed to use a methodology for relevant variable selection, thatwill seek the best prediction of engine test performance, whileoptimizing the design, number, nature, and/or cost of the test screenerresults. While in the past it has been useful to use the formulator'sexperience to determine the more predictive variables, there are manymathematical techniques to also make this selection.

For data sets comprising a limited numbers of measurements (e.g., <100),an “all possible combinations (APC) of variables” approach (e.g.,regressions or classifications) may be done with typical desktopcomputational power. These approaches typically attempt to find a fixednumber of variables, e.g., n, and try the regression, or classification,in all possible combinations of the original available, measuredvariables. For example, if 10 screener test results are available, and amodel with just 5 variables is desired, it is possible to trycombinations [1 2 3 4 5], [1 2 3 4 6], [1 2 3 4 7], [1 2 3 4 10], . . .all the way until [6 7 8 9 10], for a total of 30240 regressions. In atypical 3.2 GHz Pentium 4 machine, that calculation can be completed injust 30 seconds.

For larger data sets, while it is possible to somewhat optimize the APCalgorithms, there are numerous strategies to identify ideal combinationsof predictive variables. For example, a forward step-wise methodologysequentially selects variables one at a time, according to theirincremental classification, or prediction value. For example, if therewere ten available Intermediate Tests (per sample), this method wouldfirst identify the best predictor of the ten. Next, the method wouldlook at the remaining nine Intermediate Tests and select the next mostpredictive Tests, that when combined with the original selected mostpredictive Intermediate Test, yields the best pair of Intermediate Testpredictors. The procedure is repeated until the desired number ofvariable, n, is selected. The number of variables may also estimated,using testing data sets, or validation procedures, do establish the bestnumber of variables to utilize.

Another example of a methodology to identify ideal combinations ofpredictive variables is popularly known as genetic algorithms. In thistype of algorithms, large numbers of random combinations of variablesare selected, and tested for their prediction ability. Those setsshowing superior predictive performance are then selected, and randomlycombined, both with each other, and with other random sets. Thisprocedure is repeated until further improvements in prediction are nolonger achieved.

Genetic algorithms (GA) are robust optimizers that are able to handlenon-monotonic, even discontinuous, objective functions and find globaloptima. The present invention's approach of coupling GA with data-drivenmodels, viz., neural nets (NN), enables exploration of the lubecomposition/property space in order to identify potentially highperforming regions where further experimentation may yield valuablediscovery. The GA suggests input parameters (or intermediate parameters)to the neural net models(s), which in turn, predict the performance ofthese suggestions. Iterative feedback of these predictions to the GAlead to the “evolution” of sets of parameters that correspond topredicted performance in the desired target range.

This is more fully explained by the following prophetic example. Imaginethat for a number of Samples, we have 100,000 Intermediate Tests, eachwith their own result of each Sample. As before, we will employ theconvention that x^(n) _(A) represents the n^(th) Intermediate TestResult on Sample A. The GA, combined with a NN, selects pairs (ortriplets or quadruplets . . . ) of Intermediate Tests and performs somemanipulation upon them to create a new variable which we will designatey^(n+m) _(A) which is equal to some f(x^(n) _(A), x^(m) _(A)), where nrepresents some n^(th) Intermediate Tests result and m represents somem^(th) Intermediate test result. If the GA uses all of the possiblecombination of Pairs then there would be 10¹⁰ y variables. Of course, arandom selection of a smaller number of y variable could also beemployed.

This new set of y variables are then fed into a neural net, as describedabove, to determine coefficients (a, b, c, d . . . n+m, as before) thatpredict the End Use Test to the desired degree of accuracy. The value ofeach coefficient determines which y variables are most predictive. TheGA takes the most predictive and also selects some random selection ofthe remaining y variables. The GA then “marries” these y variables innew pair (triplet, quadruplet, etc.) combinations and produces offspringsuch that z^(n+m) _(A) which is equal to some f(y^(n) _(A), y^(m) _(A)).The GA then sends these z variables to the neural net and repeats thisentire process until the most predictive set of combinations ofIntermediate Tests has been selected.

It is clear that the present invention is not limited to just neuralnetwork models, but may be applied to any other regression model.Furthermore, the current method has the potential of being more robustthan many regression methods, as a misclassification of one, or even ahandful, of results would not seriously impair the determination of thepreferred regions. Moreover, the inventors note that this methodologyenables models that are more parsimonious than that of regression, as itdoes not force preconceived function shapes to the relationship betweenscreener tests and the engine and rig test results.

The classification model approach does not significantly limit theability to obtain several levels of performance out of a model. Thepresent method perceives gradations of success as well as a simplepass/fail model. Contours in the X-space of single bench test resultsmay be developed for each desired level of performance, or a simplecontinuous regression model may be used to determine the contours.

Further, the present invention is not limited to linearly modeledspaces. Indeed, lubricant formulation is rife with examples ofnon-linear responses and therefore the method of the current inventionis particularly suited to lubricant formulations. For example, even asimple wear rate analysis demonstrates the non-linear response oflubricants. In the wear rate scenario, there is a threshold level of anadditive that is needed before a desired performance is reached. Belowthat level, there will be no or little response, and above it thedesired performance level is reached. However, because of other factors,failure can occur when too high a level of the additive is in theformulation.

In the wear rate example shown in FIG. 3, a low wear rate is desired. Atvery low concentrations of the extreme pressure (EP) additive, there issignificant and unacceptable wear. At high concentrations, the wear rateis unacceptable due to chemical wear. Thus, a non-linear model would benecessary to properly understand optimal additive addition to achievethe minimum wear rate.

In formulating a lubricant, the goal is to achieve optimal performance.To determine the absolute minimum in the test results shown in FIG. 3,ideally one would make multiple blends between 1×10⁻⁴ to 5×10⁻⁴ molefraction and test them in multiple wear tests. This is not feasibleusing traditional blending and testing. However, by using methods thatproduce and test small amounts of a large numbers of samples, the numberof blends and evaluating tests used can be greatly increased, thus beingable to achieve the optimal performance level desired. Although thisoptimization may be performed by data mining of existing formulationdatabases, one particularly effective embodiment of the currentinvention applies high-throughput methods to create and/or testlubricant formulation samples. The introduction of these methods tolubricant research is only made feasible by the methodology of thecurrent invention.

In one embodiment, the present invention therefore is a method todetermine at least one member of the Elemental Set of Intermediate Testswhich will predict End-use Test Results for lubricants which comprises:

-   -   a. Defining the members of an Elemental Set of Samples or        Components having at least two members;    -   b. Defining the members of a set of End-use Test Results having        at least two members,        -   such that a first member of said Elemental Set of End-use            Test Results corresponds to a first member of said Elemental            Set of Samples, and        -   such that a second member of said Elemental Set of End-use            Test Results corresponds to a second member of said            Elemental Set of Samples,        -   wherein said first member of each set is not the same as            said second member of each set;    -   c. Selecting a universe of intermediate tests that may contain        appropriate members of said Elemental Set of Intermediate Tests;    -   d. Performing one or more of said intermediate tests upon at        least two members of said Elemental Set of Samples;    -   e. Determining said predictive member or members of said        Elemental Set of Intermediate Tests by using a predictive model;    -   f. Optionally, refining said predictive members of said        Elemental Set of Intermediate Tests using a variable selection        algorithm.

In another embodiment, the present invention is a method to determinenew relationships between existing bench test data that will accuratelypredict End-use Test Results for lubricants, comprising:

-   -   a. Defining the members of an Elemental Set of Samples, such        that each member of the Elemental Set of Samples has at least        two associated members in the Elemental Set of Intermediate        Tests and at least one associated member in the Elemental Set of        End-use Tests.    -   b. Creating new members for the Elemental Set of Intermediate        Tests by selecting combinations (or functional combinations) of        said at least two associated members in the Elemental Set of        Intermediate Tests which correspond to a specific member of said        Elemental Set of Samples.    -   c. On at least two of said newly created members of Elemental        Set of Intermediate Tests, use a predictive model to determine        the most predictive member(s) of the Elemental Set of        Intermediate Tests.    -   d. Optionally, refining the members (or relationship of the        members) of the Elemental Set of Intermediate Tests using a        variable selection algorithm.        This embodiment of the current invention is illustrated by the        following Example.

EXAMPLE 1

Various bench tests, known as the Mobil Thin-Film Oxidation Tests(“tfo”), were conducted on various lubricant samples. The test wasdeveloped to permit the study of controlled oxidation and depositformation under high temperature, short contact time conditions. Thetest oil is subjected to oxidizing conditions as a thin dynamic filmpassing over a rotating aluminum disk, which is maintained at anelevated test temperature while air is passed over the surface of theoil film.

The test oil is circulated continuously from a reservoir by a gear pumpthrough a delivery tube to the center of a conical-faced disk. Anelectrical heating element inside the delivery tube is generally used toraise the temperature of the test oil to 300° C. to permit precisecontrol of the temperature at the disk surface. The disk is heated by aceramic heater mounted behind it. A thermo-couple is embeddedimmediately beneath the surface of the disk to provide read-out andinput signal to a temperature controller. Typically, disk temperature isset in the range from about 315 to 360° C. The disk is rotated at 2500RPM, which generates a thin oil film across the surface of the disk. Asthe oil is spun off the disk, it is caught by a water-cooled collector,which quenches the oxidation reaction. The oil is then returned to thereservoir. An excess of air is supplied to the surface of the oil filmthrough a line and an air pump. The air supply is purified by a seriesof adsorbents before entering the reaction zone. A further descriptionof the testing are described in SAE (Society of Automotive Engineers)Technical Paper 851797, “Development of a High Temperature Jet EngineOil—Laboratory and Field Evaluation”, available at on-line athttp://www.sac.org/technical/papers/851797.

For grading, the disks are radially divided into 6 equal sections andthen each section is visually assigned a rating. Thus, the tfo1 test isthe rating for the innermost section, the tfo2 is for the next outerband, etc. Each band is provided a visual rating from 0 to 100, with 100being the cleanest. A visual rating of 0 would indicate heavy blackdeposits. A rating of 25 would be given to an area that exhibitedmedium-dense opaque brown or black deposits. A rating of 50 would begiven to indicate semi-transparent brown deposits. A rating of 75indicates transparent yellow-brown deposit. A rating of 100 indicates aclean, shiny surface. Ratings may be interpolated as necessary tocapture a correct visual picture of the rated area. If a rated areaexhibits more than one rating, a weighted average, corresponding to thepercentage surface area at each rating, should be taken.

For this example, the Volkswagen GTI End-use test (“VWTDi2 engine test”)was also performed upon each lubricant sample. The Volkswagen GTIEnd-use test is described in CEC (Coordinating European Counsel)Publication L78-T-99. While far too lengthy to describe in thisdisclosure, one of ordinary skill in the art recognizes that the presentinvention does not rely upon this specific End Use Test, but upon anyEnd Use Test that the person wishes to model.

As can be seen from the graphs of FIGS. 4 to 9, none of these six benchtests demonstrated any ability to predict the results of the VWTDi2engine test. Employing the above embodiment of the present invention,the inventors created a universe of Intermediate Tests by creating aseries of two-dimensional pairings of the bench tests. Thus, 30three-dimensional plots were developed consisting of the results of afirst bench test on the X-axis, a second bench test on the Y-Axis, andthe results of the VWTDi2 engine test on the Z-Axis. For example, forthe first of the set of Intermediate Tests, the results of the tfo1 testwere plotted against the results of the tfo2 test and the VWTDi2 enginetest. Likewise for the second of the set of Intermediate Tests, theresults of the tfo1 test were plotted against the results of the tfo3test and the VWTDi2 engine test. This was continued until all possibletwo-space combinations were created.

The inventors then modeled each of a set of Intermediate Tests (andcombinations thereof), using a Linear Discriminate Analysis ClusterMethod, to determine which test(s), or combination of tests, exhibitedthe best predictive ability to the End-use qualifying Test. Theinventors were quite surprised to find that a combination of twointermediate tests, tfo1 and tfo5, each non-predictive by itself, waspredictive in their combination.

FIG. 10 illustrates this case. This graph provides three representationsof the tfo1 test (plotted on the x-axis) vs. the tfo5 test (plotted onthe y-axis), each data point indicating whether the VWTdi2 test waspassed or failed. Each individual graph has an overlay showing thepredictive correlations using linear and non-linear predictive models.As can easily be seen by the ellipses of the first graph, the model haspredicted a two-dimensional space of the tfo1 and tfo5 bench tests thatis predictive of passing the VWTDi2 engine test. Thus, while none of thebench tests was individually predictive, in a simple two-dimensionalcombination, these bench tests were predictive. Moreover, graphs two andthree of this figure demonstrate a linear and non-linear predictivetechnique further refining the accuracy of the combined tfo1 and tfo5predictive model.

One of ordinary skill in the art realizes that these combinations thatform the universe of members of the Elemental Set of Intermediate Testsneed not be one-to-one, or indeed even linear. Also it is clear thatthese combinations do not have to be limited to a simple 3 dimensionalspace, but can encompass up to an n+m−1 dimensional space where nrepresents the number of original bench tests and m represents theoriginal number of end-use qualifying tests. Thus, in this case, thereare 6 original intermediate tests (tfo1 to tfo6) and one originalend-use qualifying test (the VWTDi2 engine test) allows for a maximum ofa 6dimensional space (5 bench tests and the VWTDi2 engine test) formodeling.

EXAMPLE 2

This method of the current invention as described in paragraph [0064] isillustrated by the following example. The inventors employed thedescribed inventive methods to determine a formulation's componentsensitivity toward the results of the same WTDi2 engine test asdescribed in the previous example. The results from the VWTDi2 enginetest were chosen as the Elemental Set of End-use Results because theinventors had access to a database with a sizable number of testresults.

To determine a proper Elemental Set of Samples and the Elemental Set ofIntermediate Tests, the inventors began with their knowledge that thecontrol of sludge, varnish and deposits formation were consideredinstrumental in passing the VW TDi2 test. Because of this relation, theinventors determined two separate Elemental Sets of Samples—a fresh oilset and an aged Catalytic Oxidation Test oil set (which corresponded tothe fresh oil set, but had been oxidized by the Catalytic Oxidation Testas described in U.S. Pat. No. 3,682,980, which is hereby incorporated byreference). Both of these Elemental Sets of Samples would point to thesame Elemental Set of End-Use Results, thus allowing for a unique set ofIntermediate Tests to be developed for each of the Elemental Set ofSamples.

To determine a possible universe of Intermediate Tests for the CatalyticOxidation Test aged oil Sample set, the inventors employed two methods.First, the inventors relied on their knowledge of sludge, varnish anddeposit formation in lubricant oil samples and the additives and basestocks that effect the formation of sludge, varnish and deposits.Second, the inventors examined the relationship between the input samplesets and engine test performance.

Sludge, varnish and deposits can be considered to be an extension ofoxidation and along with viscosity and volatility changes are aconsequence of the molecular changes that occur in the lube as it issubjected to heat, oxygen, combustion and the engine environment ingeneral. The inventors considered from the literature possiblemechanisms of the molecular changes expected to occur in the formationof sludge, varnish and deposits. Reviewing the literature, the inventorsdeveloped a suggested mechanistic model for the formation of sludge,varnish and deposits. FIG. 11 depicts this mechanistic model. Using thismodel, the inventors identified molecular level measurements needed asindicators of volatility, viscosity increase and the formation ofsludge, varnish and deposits to be an extension of oxidation, and alongwith viscosity and volatility changes are a consequence of the molecularchanges that occur in the lube as it is subject to heat, oxygen,combustion and the engine environment in general.

The mechanism shown in FIG. 11 demonstrates how certain functionalities(oxygen, nitrogen and unsaturation) are added to the lubricant moleculeresulting in a change in its molecular weight. At the same time, theantioxidants in the lubricant are being depleted. The combination ofthese two factors results in changes in polarity, particulate content,solubility, surface activity and chemistry of the lubricant. Themechanism serves as the basis to identify a set of Intermediate Teststhat are predictive of the End-use Test Results, which are listed inTable 1.

These tests were run on 21 samples, and the results are provided inTable 2. Neural net models were developed using the methods set out byJ. Leonard & M. A. Kramer, Computers and Chemical Engineering, v. 14,#3, pp. 337-341, 1990, combined with the following cross-validationmethodology. As there were 22 samples for which engine performance datawere available, 22 different models (constructed with identicalarchitecture) were developed for each of the two cases, i.e., for usedand fresh oil properties, using 21 data points (One sample was notincluded in each run) for training each model and validating each modelwith the remaining datum. The training process for each of these modelswas terminated when the prediction error on the validation datum wasminimized. The final models were then trained on all 22 data, to ensurethat the training process did not extend beyond the point where theconvergence exceeded the level dictated by the average validation error.This final process prevents over-fitting the data.

Using these neural nets, a predicted value for the VW TDi2 tests wasdeveloped for each member of the sample set. As one of ordinary skill inthe art knows, the actual neural net is a remarkably complex set of codeinstructions that cannot be easily reproduced here. However, using thetechniques of the above paragraph on the data supplied in Table 2, thesame neural nets may be developed.

From these neural nets, these inventors realized that two separategroupings of the original Sample Elemental Set could be predictive ofthe VW TDi2 end-use test result: a fresh oil group and a CatalyticOxidation Test aged oil group. In the fresh oil group, the neural netidentified the parameters of the amount of calcium and magnesiumdetergent, DPA (alkylated diphenyl amine) antioxidant measured in an IRspectrum at 1600 cm−1, and dispersant measured in an IR spectrum at 1230cm−1 as predictive. In the Catalytic Oxidation Test aged oil group, theneural net identified the parameters of the change in the Total AcidNumber (TAN), the saturation, the Aromatic content and the Polar contentof the Catalytic Oxidation Test aged oil.

The actual VW TDi2 test result for each sample was collected and ispresented as Table 3. These actual test results were compared to thepredicted values for both the fresh oil and Catalytic Oxidation Testaged oil groups. FIG. 12 demonstrates that both neural net models (thepredicted values for the fresh and Catalytic Oxidation Test aged oilsets) accurately predict the pass/fail parameters of the actual VW TDi2engine test data. Specifically, this figure shows that for the fresh oilmodel, the neural net equations correctly predicted a pass or failure (avalue of greater than or less than 60, respectively). This figure alsodemonstrates that the Catalytic Oxidation Test aged oil neural net modelwas almost as good, making a correct prediction 75% of the time. As canbe seen from this example, once the Elemental Set of Intermediate Testsis established and correlated to the End-use qualifying test results,the present invention allows one to accurately test unknown samples todetermine their ability to pass the End-use qualifying tests.

In another embodiment of the present invention, a subset of the membersof the End-use qualifying data may be used to establish the members ofthe Elemental Set of Intermediate Tests. Once those members areestablished to the desired level of confidence, the remainder of thesubset of End-use qualifying test results could be used to predictcomponents of the members of the Elemental Set of Samples. Componentpredictions could be made because the different portions of theElemental Set of Intermediate Tests would become important bydifferentiating between the End-use members. This method comprises:

-   -   1. Defining the members of the Elemental Set of Intermediate        Tests using a subset of members of the Elemental Set of End-use        test results as per the method of paragraph [0053].    -   2. Defining the members of a subset of the Elemental Set of        End-use test results that were not used to establish the members        of the Elemental Set of Intermediate Tests.    -   3. Using a predictive model on at least two members of the        Elemental Set of Intermediate Tests and at least two members of        the unused subset of the Elemental Set of End-use results to        determine the more predictive members of the Elemental Set of        Intermediate tests.    -   4. Optionally, refining the members of the Elemental Set of        Intermediate Tests using a variable selection algorithm.

EXAMPLE 3

Divide the members of the Elemental Set of End-use test results into atleast two subsets. The first subset would include all members in whichthe End-use qualifying test was barely passed. The second set wouldinclude all members in which the End-use qualifying test was stronglypassed. Intermediate subsets could be created if so desired. Using themembers of the Elemental Set of Intermediate Tests as described in theabove method, determine which samples (or which components of whichsamples) would be associated with each subset. The components or mixtureof components, selected by the genetic algorithms would establish therelationship between components of samples which permit the End-usequalifying tests to be passed.

EXAMPLE 4

Using the data generated in Example 2, in this example, the inventorsanalyzes the IR spectra to find recurring sharp spikes or ranges thatwere predictive of either passing or failing the tests. The inventorsthen would search to find what other components would exhibit the samespikes or ranges in those intermediate tests. New lubricant formulationsare then prepared with those identified components and tested with theIntermediate tests to verify the efficacy in predicting the End-usetests results. Finally, the inventors perform End-use tests to verifythe efficiency of the new, “out of the box”, components added to theformulation.

Another embodiment of the present invention is a method for determiningthe relationship between a set of Intermediate tests and a set ofEnd-use tests for lubricant samples comprising:

-   -   1. For each sample, a means for producing results from at least        two tests selected from the group consisting of:        -   i. Wear Resistance        -   ii. Friction Reduction        -   iii. Thermal Stability        -   iv. Oxidative Stability        -   v. Elastomer Compatibility        -   vi. Viscosity        -   vii. Volatility        -   viii. Corrosion Resistance        -   ix. Metal Passivation        -   x. Miscibility        -   xi. Visual Appearance        -   xii. Chemical Composition        -   xiii. Acidity        -   xiv. Total Base Number (TBN)        -   xv. Total Acid Number (TAN)        -   xvi. Deposit Forming Tendencies;    -   2. At least one End-use test result corresponding to each of        said samples;    -   3. An analyzer employing predictive models wherein said        predictive model determines said relationship between said        Intermediate tests and said End-use test or tests;    -   4. Optionally, a set of algorithms to determine the composition        or components of additional formulated samples determined by        backfitting unknown samples or components that correspond to the        results of the most predictive Intermediate tests.

FIG. 13 details one possible non-limiting form of this embodiment.Components (1) are combined at a blending station (3) to create anElemental Set of Samples. While the invention allows for any number ofcandidate samples (greater than one) to be produced, FIG. 13 representsa two-dimensional array of 60 samples. Sample sizes, if desired, mayvary as appropriate for the test to be performed. As shown in thefigure, more than one array may be produced. Each array is thensubjected to at least one test from the Elemental Set of IntermediateTests (5) which produces results (7) corresponding to each sample. Suchtesting may occur simultaneously (in parallel) or in sequence. While thecurrent embodiment anticipates the desirability of a mechanical means totransfer the samples between tests, any means, including manual, may beemployed. Standard and non-limiting members of the Elemental Set ofIntermediate Tests for this embodiment may include for example oxidationtests, functionality (Carbonyl) formation, residual antioxidancy andstability tests, molecular weight, Chemical Fraction Analysis (GPC)tests, Noack Volatility as determined by Mass Spectrometry (MS), ThermalGravimetric Analysis (TGA) and Purge and Trap Gas Chromatography (P&TGC), Iatroscan to determine the polar fraction of the molecules,viscosity tests and UV-VIS tests to determine aromatics concentration,color and turbidity. Other tests could also include TAN, TBN, seal swelltests, metal and copper corrosion tests, rust inhibition, antiwear, andelemental analysis tests.

The results (7) are placed in a database or data storage device (9)which either does or will contain the results of the Elemental Set ofEnd-use Tests (11). In this embodiment, the database (9) also containspredictive models that determine the predictive efficacy of each test inthe Elemental Set of Intermediate Tests (5) through a feedback process(13). The predictive models employed may be any of those hereindiscussed or any known to one of ordinary skill in the art. However, oneof ordinary skill in the art may easily see that the software to performthe predictive models may be separated from the database or indeed eachmodel may be performed in a different and separate manner from the next.

The embodiment in FIG. 13 also includes an option feedback loop (15)which represents the additional genetic algorithms that could be used topredict unusual or unknown members of the Sample Elemental set asdescribed in Example 4.

Tests known to be useful as members of the Elemental Set of IntermediateTests are described below, but are in no way limit the set of possibletests useful as members of the Elemental Set of Intermediate Tests.

Antioxidancy and Oxidative Stability

Lubricants must exhibit good antioxidancy properties. As lubricants agein engines, industrial equipment and bench test equipment, reactivespecies are generated such as radicals, hydroperoxides, and organicnitrates. These reactive species cause the lubricant to oxidize (formoxidation products) and degrade in performance over time. The capacityof a lube to resist oxidation is called antioxidancy or oxidativestability. Reactions involving radicals are the most difficult tocontrol.

The lubricant's capacity to control radical chemistry is a type ofantioxidancy or oxidative stability that is measured by the HighPressure Differential Scanning Calorimetry test (HPDSC). This testessentially measures the amount of antioxidancy or oxidative stabilityan oil has for controlling the build up of free radicals and thusslowing oxidation. Hydroperoxides and organic nitrates also causeoxidation but they react much differently with the lube than radicals.The lubricant's capacity to control hydroperoxide and organic nitratechemistry is the second type of antioxidancy or oxidative stability andit is different than the lubricant's capacity to control radicals.Antioxidants that control radicals, hydroperoxides and organic nitratesare all different in their chemistry.

In conventional lubricant bench tests, antioxidancy is measured by ASTMtests such as D943, D2272, D2070, D2893, D4636, D5846, and D6514. Thesetests do not discriminate between radical control or hydroperoxide andorganic nitrate control. Nor do these tests follow the evolution ofbasic oxidation products. Therefore it is difficult to make adjustmentsin lubricant chemistry to compensate for lack of antioxidancy oroxidative stability when the type of chemistry required is not known.

The inventors have found that antioxidancy or the oxidative stability ofa lubricant may be determined in a high throughput environment usingmethods such as FTIR, PDSC, and TGA. Including these methods within thescope of this invention provides techniques for a formulator to employHTE techniques to quickly evaluate multiple formulations.

Oxidative stability or residual antioxidancy is normally measured bytests such as ASTM tests D943, D2272, D2070, D2893, D4636, D5846, andD6514. The inventors have found that oxidative stability may bedetermined in a high throughput environment using GC/MS to measure thedegradation rates of cumene hyperoxide (“CHP”) and ethylhexyl nitrate(“EHN”). These degradation rates directly correlate to the oxidativestability of a lubricant. Including this method in the current inventionprovides one embodiment for a formulator to employ HTE techniques toquickly evaluate multiple formulations.

Chemical Fraction Analysis (GPC)

The chemical makeup of the base oil lubricant is critical to thefunction of that lubricant. Because of this, several methods have beenused to determine the fraction of naphthenes (C_(N)), aromatics (C_(A))and saturates (C_(P)) in the base oil of the lubricant. These fractionsmay be determined by measuring the polar fractions (polarity) of thevarious molecular types present in the lubricants. Polarity is alsoimportant as it provides another measure of the oxidative stability ofthe lubricant. Formulators currently measure polarity using tests suchas ASTM test methods D3328 and D3524. These tests are also designed toseparate volatile components, and therefore are not amenable to thehigher molecular weight nonvolatile fractions. The ASTM D4124 test canseparate the higher molecular weight nonvolatile fractions, but the testwas designed specifically for separating asphalts into four chemicallydistinct fractions. Also miniaturized techniques such as HPLC still areinconvenient for HTE because they take hours to perform.

The inventors have found a method of determining the polar fractions ofa lubricant by the use of latroscan thin layer chromatography. Thelatroscan of lubricants is particularly adapted to HTE applications asit can be accomplished in minutes. Including this method in thisinvention provides one method for a formulator to employ HTE techniquesto quickly evaluate multiple formulations and provide advantages overthe other methods discussed previously for measuring polarity.

Friction Reduction and Wear Resistance

The ability to reduce friction and resist wear is a primary function ofa lubricant. Formulators currently measure wear resistance and frictionreduction using tests such as ASTM test methods D2670, D2783, D3702,D4172, D4304, D5183, D5302, D5707, D5968, D6425, and D5620. Theinventors have found that both wear resistance and friction reductioncan be measured in a HTE environment using nano and micro indentertechniques. Including these methods in the current invention providesone embodiment for a formulator to employ HTE techniques to quicklyevaluate multiple formulations.

Additional Lubricant Properties

Formulators are also concerned about making high throughput measurementsof sludge, varnish and deposit formation, insolubles, sediment, TAN,TBN, chemical analysis, seal swell and the corrosivity of metals.Formulators currently measure sludge, varnish and deposit formationusing ASTM tests such as D5302, and D4859. Miscibility, insolubles andsediment measurements are made using ASTM measurements such as D893,D6560, and D2273. TAN and TBN are measured using ASTM tests such as D94,D664, D5770, and D5984. Chemical analysis measurements are made usingtests such as ASTM tests D5291, D2622, D5185. Seal swell is measuredusing tests such as ASTM test D4289. Similarly, Critical Metal andCopper Corrosion and Rust inhibition tests are conducted in accordancewith tests such as ASTM D2649, D4636, D5968, D5969, D6547, D6557 andD6594.

NOACK Volatility

Those skilled in the art recognize that the NOACK volatility of alubricant has become a key measure of expected engine performance.Formulators currently measure NOACK volatility and volatility in generalby using ASTM tests such as D2715, D5291 and D5800. However, theinventors have discovered a method to determine the NOACK volatilityusing Purge and Trap Gas Chromatography (P&T GC), Thermal GravimetricAnalysis (TGA) and Mass Spectrometry (MS). Including this method in thecurrent invention provides one embodiment for a formulator to employ HTEtechniques to quickly evaluate multiple formulations.

Color and Turbidity

After stressing a lubricant, the color and turbidity of a lubricant areimportant predictors of final lubricant performance. Color is determinedby ASTM test D1500. Including these methods in the current inventionprovides one embodiment for a formulator to employ HTE techniques toquickly evaluate multiple formulations.

Other Properties

Some properties, such as viscosity, have previously been successfullyminiaturized and made high throughput. Typical ASTM tests used tomeasure viscosity are: D2270, D445, D2422, D2532, D2983, D5133, D5763,D6022 and D6821.

1. A method to determine which members, individually or in combination,of an Elemental Set of Intermediate Tests are predictive of an ElementalSet of End-Use Test Results, the method comprising: providing anElemental Set of Samples having at least a first member and a secondmember; providing an Elemental Set of End-Use Test Results for saidElemental Set of Samples, the Elemental Set of End-Use Tests Results,having at least a first and a second member, such that the first memberof said Elemental Set of End-Use Test Results corresponds to said firstmember of said Elemental Set of Samples and said second member of saidElemental Set of End-Use Tests Results corresponds to said second memberof said Elemental Set of Samples, wherein said first member of each setis not the same as the second member of each set; selecting an ElementalSet of Intermediate Tests having a plurality of members; obtaining fromsaid selected Elemental Set of Intermediate Tests, a set of IntermediateTest Results; subjecting the Intermediate Test Results, individually orin combination, to regression analysis to determine which of the membersof the Elemental Set of Intermediate Tests is predictive of theElemental Set of End-Use Test Results for the Elemental Set of Samples;and outputting the members of the Elemental Set of Intermediate Testsfrom the regression analysis that are predictive of the Elemental Set ofEnd-Use Test Results for the Elemental Set of Samples.
 2. The method ofclaim 1 wherein the Elemental Set of Intermediate Tests is selected fromtests capable of producing substantial quantities of data over a broadrange.
 3. The method of claim 2 wherein the regression analysis isselected from generalized linear regression analysis and non-linerregression analysis.
 4. The method of claim 2 wherein the regressionanalysis is a neural net regression analysis.
 5. The method of claim 2wherein the Intermediate Test Results are obtained by high throughputexperimentation.
 6. A method for predicting whether a candidate samplewill meet an Elemental Set of End-Use Test Results comprising: (a) usingthe method in any one of claims 1 to 5 to determine which members,individually or in combination, of an Elemental Set of IntermediateTests are predictive of an Elemental Set of End-Use Test Results; (b)performing each of said tests determined in step (a) on each of themembers of said Elemental Set of Samples to obtain Intermediate TestResults; and (c) comparing the Intermediate Test Results of step (b) tothe Elemental Set of End-Use Test Results whereby conformance ispredictive of the candidate sample meeting the Elemental Set of End-UseTest results.
 7. A method for predicting whether candidate lubricantsamples will pass a selected end use test for lubricants comprising: (a)determining which individual or combination of a plurality ofintermediate tests provides a test result that correlates with apass/fail criteria for the selected end-use test for lubricants; (b)subjecting the candidate lubricant samples to the individual orcombination of a plurality of intermediate tests determined in step (a);(c) comparing the test result of the candidate lubricant samples withthe pass/fail criteria thereby determining whether the candidatelubricant samples will pass the selected end-use test; and (d)outputting the candidate lubricant samples which pass and fail thepass/fail criteria of the selected end-use test determined in step (c).8. The method of claim 7 wherein the intermediate tests are selectedfrom the group consisting of tests designed to provide, in a laboratoryenvironment, a measure of a property or performance feature of thelubricant sample.
 9. The method of claim 8 wherein the property orperformance feature is at least one of wear, friction, viscosity,oxidation stability, thermal stability, sludge, varnish formation,deposit formation, elastomer compatibility, volatility, corrosion, rust,miscibility or solubility, visual appearance, chemical composition,scuffing, acidity increase, soot formation, storage stability,hydrolytic stability, color-light stability, low temperature fluidity,demulsibility, emulsibility, foam formation, air entrainment and acutetoxicity.
 10. The method of claim 8 wherein the intermediate tests areselected from the group consisting of ASTM tests, SAE (Society ofAutomotive Engineers) tests, and combinations thereof, wherein the SAEtests are defined in SAE Technical Paper 851797 entitled “Development ofa High Temperature Jet Engine Oil—Laboratory and Field Evaluation”. 11.A method to determine which combination of members of an Elemental Setof Intermediate Tests have a high correlation with an Elemental Set ofEnd-Use Test Results, the method comprising: obtaining for each of aplurality of Samples a set of Intermediate Test Results from saidElemental Set of Intermediate Tests; obtaining for each of saidplurality of Samples an Elemental Set of End-Use Test Results; selectingfrom said Set of Intermediate Tests two or more members which incombination correlate most closely with said Elemental Set of End-UseTest Results for each of said plurality of Samples; and outputting theselected two or more members which in combination correlate most closelywith said Elemental Set of End-Use Test Results for each of saidplurality of Samples.
 12. The method of claim 11 wherein saidcombination of said Set of Intermediate Tests is selected by randomlycombining two or more members of said Set of Intermediate Tests anddetermining how closely the combination correlates with said ElementalSet of End-Use Test Results and repeating the procedure with otherrandom sets to determine which combination corresponds more closely. 13.The method of claim 12 wherein said combination is selected using avariable selection algorithm.
 14. The method of claim 13 wherein thealgorithm is an all possible combination algorithm.
 15. The method ofclaim 14 wherein the algorithm is a genetic algorithm.
 16. The method ofclaim 15 wherein said Elemental Set of End-Use Test Results is obtainedby subjecting said Samples to said Intermediate Tests using highthroughput experimentation.
 17. The method of claim 15 furthercomprising formulating the predicted blend and testing the blend usingsaid combination of Intermediate Tests for compliance with a pass resultfor said End-Use Test.
 18. The method of claim 16 further comprisingrepeating said selecting, formulating and testing steps until a passresult is achieved.
 19. A method for selecting components and componentamounts in lubricants, functional fluids or greases comprising:obtaining for a plurality of blends of lubricants, functional fluids orgreases with varying components and component levels a plurality ofIntermediate Test Results from a set of Intermediate Tests and at leastone End-Use Test Result from an End-Use Test; determining whichcombination of two or more Intermediate Tests for each blend correlateswith a pass result for said End-Use Test; identifying the componentlevels for each blend that corresponds to an End-Use Test Result;applying a selection algorithm to the components or mixture ofcomponents and levels to predict new blends that will pass said End-UseTest; and outputting the components or mixture of components and levelsof new blends predicted to pass said End-Use Test.
 20. The method ofclaim 19 wherein the intermediate tests are selected from the groupconsisting of tests designed to provide, in a laboratory environment, ameasure of a property or performance feature of the lubricant sample.21. The method of claim 20 wherein the property or performance featureis at least one of wear, friction, viscosity, oxidation stability,thermal stability, sludge, varnish formation, deposit formation,elastomer compatibility, volatility, corrosion, rust, miscibility orsolubility, visual appearance, chemical composition, scuffing, acidityincrease, soot formation, storage stability, hydrolytic stability,color-light stability, low temperature fluidity, demulsibility,emulsibility, foam formation, air entrainment and acute toxicity. 22.The method of claim 19 further comprising subjecting the blend to saidEnd-Use Test to determine actual correlation.